Analyzing economic trends in Nepal

# Import packages
#install.packages("corrplot")
library(dplyr)
library(data.table)
library(ggplot2)
library(pastecs)
library(corrplot)
#library(ggthemes) # For appearance of plot like theme in ggplot2

# Setting environment
# remove(list=ls())
# setwd("C:\\Users\\sunil\\Downloads\\College\\DAV\\Project")
# make evironment not to change large number to exponential
options(scipen = 999)

# Import dataset
nepal_dt <- read.csv("Source Dataset-API_NPL_DS2.csv", skip=4, header=TRUE, stringsAsFactors = FALSE)
meta_country <- read.csv("MetaData_Country.csv", header=TRUE, stringsAsFactors = FALSE)
meta_indictr <- read.csv("MetaData_Indicator.csv", header=TRUE, stringsAsFactors = FALSE)
nepal_dt

meta_country

meta_indictr

Data Preparation: Preparing data after the import

temp_df = filter(nepal_dt, grepl("tax", tolower(IndicatorName), fixed = TRUE) | grepl("tax", tolower(IndicatorCode), fixed = TRUE))
nepal_df <- temp_df
nepal_df

dim(nepal_df)

[1] 53 66

temp_df = filter(nepal_dt, grepl("gdp", tolower(IndicatorName), fixed = TRUE) | grepl("gdp", tolower(IndicatorCode), fixed = TRUE))
nepal_df <- rbind(nepal_df, temp_df)
nepal_df

dim(nepal_df)

[1] 143  66

temp_df = filter(nepal_dt, grepl("employment", tolower(IndicatorName), fixed = TRUE) | grepl("employment", tolower(IndicatorCode), fixed = TRUE))
nepal_df <- rbind(nepal_df, temp_df)
nepal_df

# Drop first and second column
nepal_df <- nepal_df[-c(1,2)]
nepal_df

# unique(nepal_df$IndicatorName)
#table(tolower(nepal_df$IndicatorName))

# Transposing the dataframe
# df_t <- (t(nepal_df))
df_t <- transpose(nepal_df)
rownames(df_t) <- colnames(nepal_df)
colnames(df_t) <- rownames(nepal_df)
View(df_t)

df_t[0,]

# Rename the columns with the first row. Columns are not properly renamed from above lines.
colnames(df_t) <- df_t[2,]
# Remove the first and second row.
df_t <- df_t[-1:-2,]
nepal_df <- df_t
View(nepal_df)

# Keep rownames as a first column
#setDT(df_t, keep.rownames = TRUE)[]
nepal_df <- cbind(names = rownames(nepal_df), nepal_df)
colnames(nepal_df)[1] <- "YEAR"
# Removing a character 'X' from the column: YEAR in nepal_df
nepal_df$YEAR <- gsub("X","",as.character(nepal_df$YEAR))
nepal_df

dim(nepal_df)[2]

[1] 243

nepal_df

# Converting columns to numeric types
#nepal_df$TM.TAX.MRCH.WM.AR.ZS = as.numeric(as.character(nepal_df$TM.TAX.MRCH.WM.AR.ZS))
#nepal_df$NY.GDP.PETR.RT.ZS = as.numeric(as.character(nepal_df$NY.GDP.PETR.RT.ZS))
nepal_df[1:dim(nepal_df)[2]] <- sapply(nepal_df[1:dim(nepal_df)[2]],as.numeric)
sapply(nepal_df, class)

                    YEAR     TM.TAX.MRCH.WM.AR.ZS        TM.TAX.MRCH.IP.ZS           NY.TAX.NIND.KN 
               "numeric"                "numeric"                "numeric"                "numeric" 
       TM.TAX.TCOM.BC.ZS        TM.TAX.MANF.BC.ZS        GC.TAX.INTT.RV.ZS     TM.TAX.MRCH.WM.FN.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
    TM.TAX.MRCH.SM.AR.ZS        TM.TAX.TCOM.IP.ZS        TM.TAX.MANF.IP.ZS           IC.TAX.GIFT.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       GC.TAX.TOTL.GD.ZS        GC.TAX.GSRV.VA.ZS        IC.TAX.LABR.CP.ZS           GC.TAX.YPKG.CN 
               "numeric"                "numeric"                "numeric"                "numeric" 
       TM.TAX.MRCH.BR.ZS           NY.TAX.NIND.CN        TM.TAX.MRCH.SR.ZS        IC.TAX.OTHR.CP.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
          GC.TAX.YPKG.ZS           GC.TAX.IMPT.ZS           GC.TAX.OTHR.CN           GC.TAX.IMPT.CN 
               "numeric"                "numeric"                "numeric"                "numeric" 
    TM.TAX.TCOM.WM.AR.ZS     TM.TAX.MANF.WM.AR.ZS              IC.TAX.PAYM           GC.TAX.EXPT.CN 
               "numeric"                "numeric"                "numeric"                "numeric" 
       IC.TAX.TOTL.CP.ZS           IC.FRM.INFM.ZS           GC.TAX.GSRV.CN           GC.TAX.INTT.CN 
               "numeric"                "numeric"                "numeric"                "numeric" 
    TM.TAX.TCOM.WM.FN.ZS     TM.TAX.MANF.WM.FN.ZS     TM.TAX.MRCH.SM.FN.ZS     TM.TAX.TCOM.SM.AR.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
    TM.TAX.MANF.SM.AR.ZS           IC.FRM.METG.ZS        GC.TAX.GSRV.RV.ZS        TM.TAX.MRCH.BC.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
          NY.TAX.NIND.CD     TM.TAX.TCOM.SM.FN.ZS     TM.TAX.MANF.SM.FN.ZS              IC.TAX.METG 
               "numeric"                "numeric"                "numeric"                "numeric" 
       GC.TAX.YPKG.RV.ZS              IC.TAX.DURS           GC.TAX.TOTL.CN        TM.TAX.TCOM.BR.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       TM.TAX.MANF.BR.ZS        TM.TAX.TCOM.SR.ZS        TM.TAX.MANF.SR.ZS        IC.TAX.PRFT.CP.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
          GC.TAX.EXPT.ZS        GC.TAX.OTHR.RV.ZS        TG.VAL.TOTL.GD.ZS           NY.GDP.MKTP.KD 
               "numeric"                "numeric"                "numeric"                "numeric" 
       NY.GDP.COAL.RT.ZS        NY.GDP.PCAP.PP.KD        NY.GDP.MINR.RT.ZS           NY.GDP.MKTP.KN 
               "numeric"                "numeric"                "numeric"                "numeric" 
    NY.GDP.DEFL.KD.ZG.AD           NV.SRV.TOTL.ZS        ER.GDP.FWTL.M3.KD     BX.TRF.PWKR.DT.GD.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.GDP.PCAP.EM.KD        SE.XPD.TERT.PC.ZS           NY.GDS.TOTL.ZS        NY.GDP.MKTP.KD.ZG 
               "numeric"                "numeric"                "numeric"                "numeric" 
       NY.GDP.DEFL.KD.ZG        SH.XPD.CHEX.GD.ZS        SE.XPD.PRIM.PC.ZS        NY.GDP.PETR.RT.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
          NY.GDP.MKTP.CD           NE.DAB.TOTL.ZS        SH.XPD.GHED.GD.ZS        SE.XPD.TOTL.GD.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
          PA.NUS.PPPC.RF        NY.GDP.MKTP.PP.KD        NY.GDP.DEFL.ZS.AD           NE.GDI.TOTL.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       GC.TAX.TOTL.GD.ZS        FS.AST.DOMS.GD.ZS        FM.AST.PRVT.GD.ZS        EN.ATM.CO2E.KD.GD 
               "numeric"                "numeric"                "numeric"                "numeric" 
       NY.GDP.PCAP.PP.CD        NY.GDP.FRST.RT.ZS           NE.GDI.FTOT.ZS        SE.XPD.SECO.PC.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       NY.GDP.MKTP.CN.AD           NV.IND.MANF.ZS           NE.TRD.GNFS.ZS        GC.REV.XGRT.GD.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       GB.XPD.RSDV.GD.ZS     EG.USE.COMM.GD.PP.KD        GC.NLD.TOTL.GD.ZS        BN.CAB.XOKA.GD.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       BG.GSR.NFSV.GD.ZS           NE.CON.PRVT.ZS        GC.LBL.TOTL.GD.ZS        FS.AST.PRVT.GD.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
    BM.KLT.DINV.WD.GD.ZS           NY.GDP.PCAP.KD           NY.GDP.FCST.CN        FS.AST.CGOV.GD.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       EN.ATM.CO2E.PP.GD     EG.GDP.PUSE.KO.PP.KD        EG.EGY.PRIM.PP.KD        GC.NFN.TOTL.GD.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       FM.LBL.BMNY.GD.ZS        NY.GDP.PCAP.KD.ZG           NY.GDP.FCST.KD        NY.GDP.TOTL.RT.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
          NY.GDP.MKTP.CN           NE.RSB.GNFS.ZS        MS.MIL.XPND.GD.ZS        NY.GDP.NGAS.RT.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
          NY.GDP.DISC.CN           NV.IND.TOTL.ZS           NE.GDI.FPRV.ZS        GC.DOD.TOTL.GD.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       FS.AST.DOMO.GD.ZS     EN.ATM.CO2E.PP.GD.KD     BX.KLT.DINV.WD.GD.ZS           NY.GDP.PCAP.KN 
               "numeric"                "numeric"                "numeric"                "numeric" 
          NY.GDP.FCST.KN           NE.IMP.GNFS.ZS           NY.GNS.ICTR.ZS           NY.GDP.PCAP.CD 
               "numeric"                "numeric"                "numeric"                "numeric" 
          NY.GDP.DISC.KN           NV.AGR.TOTL.ZS        CM.MKT.TRAD.GD.ZS        CM.MKT.LCAP.GD.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
              PA.NUS.PPP        NY.GDP.MKTP.PP.CD           NY.GDP.DEFL.ZS           NE.EXP.GNFS.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
          NY.GDP.PCAP.CN           NY.GDP.FCST.CD           NE.CON.TOTL.ZS        GC.AST.TOTL.GD.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       EG.GDP.PUSE.KO.PP           NE.CON.GOVT.ZS        GC.XPN.TOTL.GD.ZS        FD.AST.PRVT.GD.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
          SL.UEM.NEET.ZS        SL.UEM.1524.FE.ZS           SL.SRV.EMPL.ZS           SL.FAM.WORK.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
    SL.EMP.TOTL.SP.FE.ZS        SL.AGR.EMPL.MA.ZS  per_lm_alllm.cov_q5_tot        SL.UEM.INTM.MA.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
          SL.TLF.PART.ZS     SL.TLF.0714.WK.MA.ZS        SL.SRV.0714.MA.ZS        SL.FAM.0714.MA.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.EMP.SELF.MA.ZS        SL.AGR.0714.FE.ZS  per_lm_alllm.cov_q1_tot        SL.UEM.TOTL.FE.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.UEM.1524.MA.ZS        SL.TLF.0714.MA.ZS        SL.IND.EMPL.FE.ZS     SL.EMP.TOTL.SP.MA.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
 SL.EMP.1524.SP.FE.NE.ZS     SL.UEM.TOTL.FE.NE.ZS     SL.UEM.1524.MA.NE.ZS        SL.TLF.0714.FE.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
 SL.EMP.TOTL.SP.MA.NE.ZS           SL.AGR.EMPL.ZS           SL.UEM.INTM.ZS           SL.SRV.0714.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
          SL.FAM.0714.ZS           SL.EMP.SELF.ZS        SL.AGR.0714.MA.ZS  per_lm_alllm.cov_q2_tot 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.UEM.TOTL.MA.ZS           SL.UEM.1524.ZS     SL.TLF.0714.SW.FE.ZS           SL.IND.EMPL.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.EMP.TOTL.SP.ZS  SL.EMP.1524.SP.MA.NE.ZS        SL.UEM.INTM.FE.ZS        SL.TLF.PART.MA.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.SRV.0714.FE.ZS        SL.FAM.0714.FE.ZS        SL.EMP.SELF.FE.ZS per_lm_alllm.cov_pop_tot 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.UEM.NEET.MA.ZS     SL.UEM.1524.FE.NE.ZS           SL.TLF.0714.ZS        SL.SRV.EMPL.MA.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.FAM.WORK.MA.ZS  SL.EMP.TOTL.SP.FE.NE.ZS        SL.AGR.EMPL.FE.ZS  per_lm_alllm.cov_q4_tot 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.WAG.0714.MA.ZS        SL.UEM.BASC.FE.ZS        SL.TLF.0714.SW.ZS        SL.SLF.0714.FE.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.EMP.WORK.FE.ZS        SL.EMP.MPYR.FE.ZS           SL.WAG.0714.ZS        SL.UEM.BASC.MA.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.SLF.0714.MA.ZS        SL.EMP.WORK.MA.ZS        SL.EMP.MPYR.MA.ZS per_lm_alllm.adq_pop_tot 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.UEM.NEET.FE.ZS        SL.TLF.0714.WK.ZS        SL.SRV.EMPL.FE.ZS        SL.FAM.WORK.FE.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.EMP.SMGT.FE.ZS           SL.AGR.0714.ZS  per_lm_alllm.cov_q3_tot        SL.UEM.TOTL.NE.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.UEM.ADVN.FE.ZS        SL.MNF.0714.FE.ZS        SL.EMP.VULN.FE.ZS     SL.EMP.1524.SP.MA.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
          SL.UEM.BASC.ZS        SL.TLF.PART.FE.ZS     SL.TLF.0714.WK.FE.ZS           SL.SLF.0714.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
          SL.EMP.WORK.ZS           SL.EMP.MPYR.ZS  per_lm_alllm.ben_q1_tot           SL.UEM.TOTL.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
       SL.UEM.ADVN.MA.ZS     SL.TLF.0714.SW.MA.ZS        SL.MNF.0714.MA.ZS        SL.EMP.VULN.MA.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
    SL.EMP.1524.SP.NE.ZS     SL.UEM.TOTL.MA.NE.ZS        SL.UEM.1524.NE.ZS        SL.IND.EMPL.MA.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
    SL.EMP.TOTL.SP.NE.ZS     SL.EMP.1524.SP.FE.ZS        SL.WAG.0714.FE.ZS           SL.UEM.ADVN.ZS 
               "numeric"                "numeric"                "numeric"                "numeric" 
          SL.MNF.0714.ZS           SL.EMP.VULN.ZS        SL.EMP.1524.SP.ZS 
               "numeric"                "numeric"                "numeric" 

# Replace NA values with 0
#nepal_df["TM.TAX.MRCH.WM.AR.ZS"][is.na(nepal_df["TM.TAX.MRCH.WM.AR.ZS"])] <- 0
#nepal_df["NY.GDP.PETR.RT.ZS"][is.na(nepal_df["NY.GDP.PETR.RT.ZS"])] <- 0
# Replace na values with 0 using is.na()
nepal_df[is.na(nepal_df)] <- 0

nepal_df

# Viewing the data after preparing it.
View(nepal_df)

Parameter Selection:

## Sample parameters selection to achieve project objective.
# GC.TAX.GSRV.VA.ZS -> Taxes on goods and services(%)
# GC.TAX.GSRV.CN
# GC.TAX.TOTL.GD.ZS -> Tax revenue (% of GDP)
# IC.TAX.LABR.CP.ZS -> Labor tax and contributions (% of commercial profits) | Labor tax and contributions is the amount of taxes and mandatory contributions on labor paid by the business.
# GC.TAX.YPKG.CN -> Taxes on income, profits and capital gains (current LCU)
# GC.TAX.IMPT.ZS -> Customs and other import duties (% of tax revenue)
# GC.TAX.IMPT.CN -> Customs and other import duties (current LCU)
# GC.TAX.EXPT.ZS -> Taxes on exports (% of tax revenue)
# GC.TAX.EXPT.CN -> Taxes on exports (current LCU)
# IC.TAX.TOTL.CP.ZS -> Total tax and contribution rate (% of profit)
# NY.GDP.MKTP.KD -> GDP (constant 2015 US$)
# NY.GDP.MKTP.KD.ZG -> GDP growth (annual %)
# SL.IND.EMPL.ZS -> Employment in industry (% of total employment) (modeled ILO estimate)
# SL.IND.EMPL.FE.ZS -> Employment in industry, female (% of female employment) (modeled ILO estimate)
# SL.IND.EMPL.MA.ZS -> Employment in industry, male (% of male employment) (modeled ILO estimate)
# SL.AGR.EMPL.ZS -> Employment in agriculture (% of total employment) (modeled ILO estimate)
# SL.AGR.EMPL.FE.ZS -> Employment in agriculture, female (% of female employment) (modeled ILO estimate)
# SL.AGR.EMPL.MA.ZS -> Employment in agriculture, male (% of male employment) (modeled ILO estimate)

## Sample parameter selection to achieve project objective.
# GC.TAX.GSRV.VA.ZS, NY.GDP.MKTP.KD  0.8481471
# GC.TAX.GSRV.VA.ZS, SL.IND.EMPL.ZS  0.8880489
# GC.TAX.GSRV.VA.ZS, SL.IND.EMPL.FE.ZS 0.8928028
# GC.TAX.GSRV.VA.ZS, SL.IND.EMPL.MA.ZS 0.8939309
# GC.TAX.GSRV.VA.ZS, SL.AGR.EMPL.ZS 0.8268747
# GC.TAX.GSRV.VA.ZS, SL.AGR.EMPL.FE.ZS 0.8333567
# GC.TAX.GSRV.VA.ZS, SL.AGR.EMPL.MA.ZS 0.8062022
# GC.TAX.INTT.RV.ZS, SL.IND.EMPL.ZS 0.727295
# GC.TAX.INTT.RV.ZS, SL.IND.EMPL.FE.ZS 0.7059692
# GC.TAX.INTT.RV.ZS, SL.IND.EMPL.MA.ZS 0.7179946
# GC.TAX.TOTL.GD.ZS, SL.IND.EMPL.ZS 0.893035
# GC.TAX.TOTL.GD.ZS, SL.IND.EMPL.FE.ZS 0.8984195
# GC.TAX.TOTL.GD.ZS, SL.IND.EMPL.MA.ZS 0.8992892
# IC.TAX.LABR.CP.ZS
# GC.TAX.YPKG.CN
# GC.TAX.IMPT.ZS
# GC.TAX.EXPT.CN
# IC.TAX.TOTL.CP.ZS

## Sample parameters selection to achieve project objective.
nepal_df <- select(nepal_df, 'YEAR', 'GC.TAX.GSRV.VA.ZS', 'GC.TAX.GSRV.CN', 'GC.TAX.TOTL.GD.ZS', 'IC.TAX.LABR.CP.ZS', 'GC.TAX.YPKG.CN', 'GC.TAX.IMPT.ZS', 'GC.TAX.IMPT.CN', 'GC.TAX.EXPT.ZS', 'GC.TAX.EXPT.CN', 'IC.TAX.TOTL.CP.ZS', 'NY.GDP.MKTP.KD', 'NY.GDP.MKTP.KD.ZG', 'SL.IND.EMPL.ZS', 'SL.IND.EMPL.FE.ZS', 'SL.IND.EMPL.MA.ZS', 'SL.AGR.EMPL.ZS', 'SL.AGR.EMPL.FE.ZS', 'SL.AGR.EMPL.MA.ZS')
nepal_df

Data Quality: Checking the data

## Checking quality of data in parameters selected.
#View(truncate(summary(nepal_df)))
#df_t <- summary(nepal_df)
#View(t(df_t))
View(summary(nepal_df))

stat.desc(nepal_df)

Correlation Analysis: Exploring relationship between employment, tax and GDP. Understanding what drives economic activity.

# Finding correlation between each columns in the dataframe
# cor(nepal_df$TM.TAX.MRCH.WM.AR.ZS, nepal_df$NY.GDP.PETR.RT.ZS)
# cor(nepal_df$GC.TAX.TOTL.GD.ZS, nepal_df$SL.IND.EMPL.FE.ZS)
View(cor(nepal_df))

# Correlation matrix plot
corrplot(cor(nepal_df), type="lower")

var(nepal_df$GC.TAX.GSRV.VA.ZS)

[1] 26.21113

# SL.IND.EMPL.ZS  NY.GDP.MKTP.KD

Time series analysis: Trends/patterns in the data over time

# autoregressive integrated moving average (ARIMA) - need to look at it
# GDP = Consumption + Investment + Government spending + Net exports
p <- ggplot(nepal_df, aes(x=nepal_df$YEAR, y=nepal_df$GC.TAX.GSRV.VA.ZS)) +
  geom_line( color="steelblue") + 
  geom_point() +
  xlab("YEAR") +
  ylab("Taxes on goods and services(%)") +
  ggtitle("Percent increase on tax on goods & services each year")
  #scale_x_date(limit=c(as.Date("1960-01-01"),as.Date("2022-12-30"))) +
  
p

# Check tax and gdp over time
coeff <- 10
tax_color <- "black"
gdp_color <- "steelblue"
ggplot(nepal_df, aes(x=nepal_df$YEAR)) +
  
  geom_line( aes(y=nepal_df$GC.TAX.GSRV.CN), size=0.5, color=tax_color) + 
  geom_line( aes(y=nepal_df$NY.GDP.MKTP.KD), size=0.5, color=gdp_color) +
  
  geom_point(aes(y = nepal_df$GC.TAX.GSRV.CN), size=2, color=tax_color) +
  geom_point(aes(y = nepal_df$NY.GDP.MKTP.KD), size=2, color=gdp_color) +
  
  scale_y_continuous(
    
    # First axis
    name = "Taxes on goods and services (current LCU)",
    
    # Second axis
    sec.axis = sec_axis(~.*1, name="GDP (constant 2015 US$)")
  ) +
#  theme_ipsum() +
  scale_x_continuous(
    name = "YEAR"
  ) +
  theme(
    axis.title.y = element_text(color = tax_color, size=13),
    axis.title.y.right = element_text(color = gdp_color, size=13)
  ) +
  ggtitle("Tax and GDP over time") +
  theme(plot.title = element_text(hjust = 0.5)) #Title to be at center

Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.

coeff <- 10
tax_color <- "black"
gdp_color <- "steelblue"
ggplot(nepal_df, aes(x=nepal_df$YEAR)) +
  
  geom_line( aes(y=nepal_df$SL.IND.EMPL.ZS), size=0.5, color=tax_color) + 
  geom_line( aes(y=nepal_df$SL.AGR.EMPL.ZS), size=0.5, color=gdp_color) +
  
  geom_point(aes(y = nepal_df$SL.IND.EMPL.ZS), size=2, color=tax_color) +
  geom_point(aes(y = nepal_df$SL.AGR.EMPL.ZS), size=2, color=gdp_color) +
  
  scale_y_continuous(
    
    # First axis
    name = "Employment in industry (% of total employment)",
    
    # Second axis
    sec.axis = sec_axis(~.*1, name="Employment in agriculture (% of total employment)")
  ) +
#  theme_ipsum() +
  scale_x_continuous(
    name = "YEAR"
  ) +
  theme(
    axis.title.y = element_text(color = tax_color, size=13),
    axis.title.y.right = element_text(color = gdp_color, size=13)
  ) +
  ggtitle("Employment in industry & agriculture over time") +
  theme(plot.title = element_text(hjust = 0.5)) #Title to be at center

coeff <- 10
ind_color <- "black"
agr_color <- "steelblue"
ggplot(nepal_df, aes(x=nepal_df$YEAR)) +
  
  geom_line( aes(y=nepal_df$SL.IND.EMPL.FE.ZS), size=0.5, color=ind_color) + 
  geom_line( aes(y=nepal_df$SL.AGR.EMPL.FE.ZS), size=0.5, color=agr_color) +
  
  geom_point(aes(y = nepal_df$SL.IND.EMPL.FE.ZS), size=2, color=ind_color) +
  geom_point(aes(y = nepal_df$SL.AGR.EMPL.FE.ZS), size=2, color=agr_color) +
  
  scale_y_continuous(
    
    # First axis
    name = "Employment in industry, female (% of female employment)",
    
    # Second axis
    sec.axis = sec_axis(~.*1, name="Employment in agriculture, female (% of female employment)")
  ) +
#  theme_ipsum() +
  scale_x_continuous(
    name = "YEAR"
  ) +
  theme(
    axis.title.y = element_text(color = ind_color, size=13),
    axis.title.y.right = element_text(color = agr_color, size=13)
  ) +
  ggtitle("Employment in industry & agriculture, females over time") +
  theme(plot.title = element_text(hjust = 0.5)) #Title to be at center

coeff <- 10
ind_color <- "black"
agr_color <- "steelblue"
ggplot(nepal_df, aes(x=nepal_df$YEAR)) +
  
  geom_line( aes(y=nepal_df$SL.IND.EMPL.MA.ZS), size=0.5, color=ind_color) + 
  geom_line( aes(y=nepal_df$SL.AGR.EMPL.MA.ZS), size=0.5, color=agr_color) +
  
  geom_point(aes(y = nepal_df$SL.IND.EMPL.MA.ZS), size=2, color=ind_color) +
  geom_point(aes(y = nepal_df$SL.AGR.EMPL.MA.ZS), size=2, color=agr_color) +
  
  scale_y_continuous(
    
    # First axis
    name = "Employment in industry, male (% of male employment)",
    
    # Second axis
    sec.axis = sec_axis(~.*1, name="Employment in agriculture, male (% of male employment)")
  ) +
#  theme_ipsum() +
  scale_x_continuous(
    name = "YEAR"
  ) +
  theme(
    axis.title.y = element_text(color = ind_color, size=13),
    axis.title.y.right = element_text(color = agr_color, size=13)
  ) +
  ggtitle("Employment in industry & agriculture, males over time") +
  theme(plot.title = element_text(hjust = 0.5)) #Title to be at center

Regression:

#help(“scale_x_continuous”)

ggplot(nepal_df, aes(x = GC.TAX.GSRV.CN, y = NY.GDP.MKTP.KD)) +
  geom_point() +
geom_smooth() + 
# Add a regression line
xlab("Taxes on goods and services (current LCU)") +
ylab("GDP (constant 2015 US$)") +
scale_x_continuous() +
scale_y_continuous() +
ggtitle("Regression: GDP x taxes on goods & services")

# Checking GDP growth on every tax % increase
# with trend line (regression line)
ggplot(nepal_df, aes(x = GC.TAX.GSRV.VA.ZS, y = NY.GDP.MKTP.KD)) +
  geom_point() +
geom_smooth() + # Add a regression line
xlab("Taxes on goods and services (% value added of industry and services)") +
ylab("GDP (constant 2015 US$)") +
scale_x_continuous() +
scale_y_continuous() +
ggtitle("Regression: GDP x taxes on goods & services")

ggplot(nepal_df, aes(x = SL.IND.EMPL.ZS, y = GC.TAX.GSRV.VA.ZS)) +
  geom_point() +
geom_smooth() + # Add a regression line
xlab("Employment in industry (% of total employment)") +
ylab("Taxes on goods and services (% value added of industry and services)") +
scale_x_continuous() +
scale_y_continuous() +
ggtitle("Regression: Tax on goods & services X Employment in industry")

ggplot(nepal_df, aes(x = GC.TAX.GSRV.VA.ZS, y = SL.AGR.EMPL.ZS )) +
  geom_point() +
geom_smooth() + # Add a regression line
xlab("Taxes on goods and services (% value added of industry and services)") +
ylab("Employment in agriculture (% of total employment)") +
scale_x_continuous() +
scale_y_continuous() +
ggtitle("Regression: Tax on goods & services X Employment in agriculture")

ggplot(nepal_df, aes(x = GC.TAX.IMPT.ZS, y = SL.IND.EMPL.ZS)) +
  geom_point() +
geom_smooth() + # Add a regression line
xlab("Customs and other import duties (% of tax revenue)") +
ylab("Employment in industry (% of total employment)") +
scale_x_continuous() +
scale_y_continuous() +
ggtitle("Regression: Customs Import duties X Employment in industry")

ggplot(nepal_df, aes(x = GC.TAX.IMPT.ZS, y = SL.AGR.EMPL.ZS)) +
  geom_point() +
geom_smooth() + # Add a regression line
xlab("Customs and other import duties (% of tax revenue)") +
ylab("Employment in agriculture (% of total employment)") +
scale_x_continuous() +
scale_y_continuous() +
ggtitle("Regression: Customs Import duties X Employment in agriculture")

ggplot(nepal_df, aes(x = SL.IND.EMPL.ZS, y = GC.TAX.EXPT.ZS)) +
  geom_point() +
geom_smooth() + # Add a regression line
xlab("Employment in industry (% of total employment)") +
ylab("Taxes on exports (% of tax revenue)") +
scale_x_continuous() +
scale_y_continuous() +
ggtitle("Regression: Exports vs Employment in Industry")

ggplot(nepal_df, aes(x = GC.TAX.EXPT.ZS, y = SL.AGR.EMPL.ZS)) +

There were 12 warnings (use warnings() to see them)

  geom_point() +
geom_smooth() + # Add a regression line
xlab("Taxes on exports (% of tax revenue)") +
ylab("Employment in agriculture (% of total employment)") +
scale_x_continuous() +
scale_y_continuous() +
ggtitle("Regression: Exports vs Employment in Agriculture")

nepal_df$GC.TAX.EXPT.CN 

 [1]          0          0          0          0          0          0          0          0          0          0
[11]          0          0          0          0          0          0          0          0          0          0
[21]          0          0          0          0          0          0          0          0          0          0
[31]   32200000   78000000  115000000  141000000  427000000  332000000  150000000  168000000  217000000  378000000
[41]  432000000  493000000  917000000  855600000  527100000  697900000  625284000  698600000  445600000  793800000
[51]  915461000  292395000  861574000  439097000 1069880000  314849716  159554771  125130000  102360000  237634000
[61]  112370000          0

#y = GC.TAX.GSRV.VA.ZS

ggplot(nepal_df, aes(x = nepal_df$SL.IND.EMPL.ZS, y = nepal_df$NY.GDP.MKTP.KD, fill = nepal_df$SL.IND.EMPL.ZS)) +
  geom_bar(stat = "identity", position = "dodge", width = 0.08) +
  #theme_bw() +
  xlab("Employment increase(%)") +
  ylab("GDP (constant 2015 US$)") +
  theme(axis.text.x = element_text(size = 10)) +
  theme(axis.text.y = element_text(size = 10)) +
  ggtitle("Bar plot: GDP vs Employment increase(%)")

ggplot(nepal_df, aes(x = nepal_df$GC.TAX.GSRV.VA.ZS, y = nepal_df$NY.GDP.MKTP.KD, fill = nepal_df$GC.TAX.GSRV.VA.ZS)) +
  geom_bar(stat = "identity", position = "dodge", width = 0.08) +
  #theme_bw() +
  xlab("Taxes on goods and services(%)") +
  ylab("GDP (constant 2015 US$)") +
  theme(axis.text.x = element_text(size = 10)) +
  theme(axis.text.y = element_text(size = 10)) +
  ggtitle("Bar plot: GDP vs Taxes on goods & services(%)")

#GC.TAX.GSRV.VA.ZS, NY.GDP.MKTP.KD

#a <- filter(nepal_df, YEAR>2012)
#select(a, GC.TAX.GSRV.CN, NY.GDP.MKTP.KD)

Cluster:

# C

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